Multiscale segmentation of the skull in MR images for MRI-based attenuation correction of combined MR/PET

Xiaofeng Yang, Baowei Fei

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


Background and objective Combined magnetic resonance/positron emission tomography (MR/PET) is a relatively new, hybrid imaging modality. MR-based attenuation correction often requires segmentation of the bone on MR images. In this study, we present an automatic segmentation method for the skull on MR images for attenuation correction in brain MR/PET applications. Materials and methods Our method transforms T1-weighted MR images to the Radon domain and then detects the features of the skull image. In the Radon domain we use a bilateral filter to construct a multiscale image series. For the repeated convolution we increase the spatial smoothing in each scale and make the width of the spatial and range Gaussian function doubled in each scale. Two filters with different kernels along the vertical direction are applied along the scales from the coarse to fine levels. The results from a coarse scale give a mask for the next fine scale and supervise the segmentation in the next fine scale. The use of the multiscale bilateral filtering scheme is to improve the robustness of the method for noise MR images. After combining the two filtered sinograms, the reciprocal binary sinogram of the skull is obtained for the reconstruction of the skull image. Results This method has been tested with brain phantom data, simulated brain data, and real MRI data. For real MRI data the Dice overlap ratios are 92.2% ±1.9% between our segmentation and manual segmentation. Conclusions The multiscale segmentation method is robust and accurate and can be used for MRI-based attenuation correction in combined MR/PET.

Original languageEnglish (US)
Pages (from-to)1037-1045
Number of pages9
JournalJournal of the American Medical Informatics Association
Issue number6
StatePublished - 2013
Externally publishedYes

ASJC Scopus subject areas

  • Health Informatics


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